OpenRaiser/Intern-Atlas huggingface dataset:
Solving $X^{q+1}+X+a=0$ over Finite Fields
Venue: arXiv cs.IT 2019
Year: 2019

Solving the equation $P_a(X):=X^{q+1}+X+a=0$ over finite field $\GF{Q}$, where $Q=p^n, q=p^k$ and $p$ is a prime, arises in many different contexts including finite geometry, the inverse Galois problem \cite{ACZ2000}, the construction of difference sets with Singer parameters \cite{DD2004}, determining cross-correlation between $m$-sequences \cite{DOBBERTIN2006,HELLESETH2008} and to construct error-correcting codes \cite{Bracken2009}, as well as to speed up the index calculus method for computing discrete logarithms on finite fields \cite{GGGZ2013,GGGZ2013+} and on algebraic curves \cite{M2014}. Subsequently, in \cite{Bluher2004,HK2008,HK2010,BTT2014,Bluher2016,KM2019,CMPZ2019,MS2019}, the $\GF{Q}$-zeros of $P_a(X)$ have been studied: in \cite{Bluher2004} it was shown that the possible values of the number of the zeros that $P_a(X)$ has in $\GF{Q}$ is $0$, $1$, $2$ or $p^{\gcd(n, k)}+1$. Some criteria for the number of the $\GF{Q}$-zeros of $P_a(x)$ were found in \cite{HK2008,HK2010,BTT2014,KM2019,MS2019}. However, while the ultimate goal is to identify all the $\GF{Q}$-zeros, even in the case $p=2$, it was solved only under the condition $\gcd(n, k)=1$ \cite{KM2019}. We discuss this equation without any restriction on $p$ and $\gcd(n,k)$. New criteria for the number of the $\GF{Q}$-zeros of $P_a(x)$ are proved. For the cases of one or two $\GF{Q}$-zeros, we provide explicit expressions for these rational zeros in terms of $a$. For the case of $p^{\gcd(n, k)}+1$ rational zeros, we provide a parametrization of such $a$'s and express the $p^{\gcd(n, k)}+1$ rational zeros by using that parametrization.
Personalizing Fast-Forward Videos Based on Visual and Textual Features from Social Network
Venue: arXiv cs.CV 2019
Year: 2019

The growth of Social Networks has fueled the habit of people logging their day-to-day activities, and long First-Person Videos (FPVs) are one of the main tools in this new habit. Semantic-aware fast-forward methods are able to decrease the watch time and select meaningful moments, which is key to increase the chances of these videos being watched. However, these methods can not handle semantics in terms of personalization. In this work, we present a new approach to automatically creating personalized fast-forward videos for FPVs. Our approach explores the availability of text-centric data from the user's social networks such as status updates to infer her/his topics of interest and assigns scores to the input frames according to her/his preferences. Extensive experiments are conducted on three different datasets with simulated and real-world users as input, achieving an average F1 score of up to 12.8 percentage points higher than the best competitors. We also present a user study to demonstrate the effectiveness of our method.
Cyber Situation Awareness with Active Learning for Intrusion Detection
Venue: arXiv cs.CR 2019
Year: 2019

Intrusion detection has focused primarily on detecting cyberattacks at the event-level. Since there is such a large volume of network data and attacks are minimal, machine learning approaches have focused on improving accuracy and reducing false positives, but this has frequently resulted in overfitting. In addition, the volume of intrusion detection alerts is large and creates fatigue in the human analyst who must review them. This research addresses the problems associated with event-level intrusion detection and the large volumes of intrusion alerts by applying active learning and cyber situation awareness. This paper includes the results of two experiments using the UNSW-NB15 dataset. The first experiment evaluated sampling approaches for querying the oracle, as part of active learning. It then trained a Random Forest classifier using the samples and evaluated its results. The second experiment applied cyber situation awareness by aggregating the detection results of the first experiment and calculating the probability that a computer system was part of a cyberattack. This research showed that moving the perspective of event-level alerts to the probability that a computer system was part of an attack improved the accuracy of detection and reduced the volume of alerts that a human analyst would need to review.
On the Performance and Energy Efficiency of the PGAS Programming Model on Multicore Architectures
Venue: arXiv cs.DC 2019
Year: 2019

Using large-scale multicore systems to get the maximum performance and energy efficiency with manageable programmability is a major challenge. The partitioned global address space (PGAS) programming model enhances programmability by providing a global address space over large-scale computing systems. However, so far the performance and energy efficiency of the PGAS model on multicore-based parallel architectures have not been investigated thoroughly. In this paper we use a set of selected kernels from the well-known NAS Parallel Benchmarks to evaluate the performance and energy efficiency of the UPC programming language, which is a widely used implementation of the PGAS model. In addition, the MPI and OpenMP versions of the same parallel kernels are used for comparison with their UPC counterparts. The investigated hardware platforms are based on multicore CPUs, both within a single 16-core node and across multiple nodes involving up to 1024 physical cores. On the multi-node platform we used the hardware measurement solution called High definition Energy Efficiency Monitoring tool in order to measure energy. On the single-node system we used the hybrid measurement solution to make an effort into understanding the observed performance differences, we use the Intel Performance Counter Monitor to quantify in detail the communication time, cache hit/miss ratio and memory usage. Our experiments show that UPC is competitive with OpenMP and MPI on single and multiple nodes, with respect to both the performance and energy efficiency.
The Semantic Mutex Watershed for Efficient Bottom-Up Semantic Instance Segmentation
Venue: arXiv cs.CV 2019
Year: 2019

Semantic instance segmentation is the task of simultaneously partitioning an image into distinct segments while associating each pixel with a class label. In commonly used pipelines, segmentation and label assignment are solved separately since joint optimization is computationally expensive. We propose a greedy algorithm for joint graph partitioning and labeling derived from the efficient Mutex Watershed partitioning algorithm. It optimizes an objective function closely related to the Symmetric Multiway Cut objective and empirically shows efficient scaling behavior. Due to the algorithm's efficiency it can operate directly on pixels without prior over-segmentation of the image into superpixels. We evaluate the performance on the Cityscapes dataset (2D urban scenes) and on a 3D microscopy volume. In urban scenes, the proposed algorithm combined with current deep neural networks outperforms the strong baseline of `Panoptic Feature Pyramid Networks' by Kirillov et al. (2019). In the 3D electron microscopy images, we show explicitly that our joint formulation outperforms a separate optimization of the partitioning and labeling problems.
Deep Context-Aware Kernel Networks
Venue: arXiv cs.CV 2019
Year: 2019

Context plays a crucial role in visual recognition as it provides complementary clues for different learning tasks including image classification and annotation. As the performances of these tasks are currently reaching a plateau, any extra knowledge, including context, should be leveraged in order to seek significant leaps in these performances. In the particular scenario of kernel machines, context-aware kernel design aims at learning positive semi-definite similarity functions which return high values not only when data share similar contents, but also similar structures (a.k.a contexts). However, the use of context in kernel design has not been fully explored; indeed, context in these solutions is handcrafted instead of being learned. In this paper, we introduce a novel deep network architecture that learns context in kernel design. This architecture is fully determined by the solution of an objective function mixing a content term that captures the intrinsic similarity between data, a context criterion which models their structure and a regularization term that helps designing smooth kernel network representations. The solution of this objective function defines a particular deep network architecture whose parameters correspond to different variants of learned contexts including layerwise, stationary and classwise; larger values of these parameters correspond to the most influencing contextual relationships between data. Extensive experiments conducted on the challenging ImageCLEF Photo Annotation and Corel5k benchmarks show that our deep context networks are highly effective for image classification and the learned contexts further enhance the performance of image annotation.
Adding Custom Intersectors to the C++ Ray Tracing Template Library Visionaray
Venue: arXiv cs.GR 2019
Year: 2019

Most ray tracing libraries allow the user to provide custom functionality that is executed when a potential ray surface interaction was encountered to determine if the interaction was valid or traversal should be continued. This is e.g. useful for alpha mask validation and allows the user to reuse existing ray object intersection routines rather than reimplementing them. Augmenting ray traversal with custom intersection logic requires some kind of callback mechanism that injects user code into existing library routines. With template libraries, this injection can happen statically since the user compiles the binary code herself. We present an implementation of this "custom intersector" approach and its integration into the C++ ray tracing template library Visionaray.
Trajectory Design for Multiple-UAV Assisted Wireless Networks
Venue: arXiv cs.IT 2019
Year: 2019

Unmanned aerial vehicles (UAVs) can enhance the performance of cellular networks, due to their high mobility and efficient deployment. In this paper, we present a first study on how the user mobility affects the UAVs' trajectories of a multiple-UAV assisted wireless communication system. Specifically, we consider the UAVs are deployed as aerial base stations to serve ground users who move between different regions. We maximize the throughput of ground users in the downlink communication by optimizing the UAVs' trajectories, while taking into account the impact of the user mobility, propulsion energy consumption, and UAVs' mutual interference. We formulate the problem as a route selection problem in an acyclic directed graph. Each vertex represents a task associated with a reward on the average user throughput in a region-time point, while each edge is associated with a cost on the energy propulsion consumption during flying and hovering. For the centralized trajectory design, we first propose the shortest path scheme that determines the optimal trajectory for the single UAV case. We also propose the centralized route selection (CRS) scheme to systematically compute the optimal trajectories for the more general multiple-UAV case. Due to the NP-hardness of the centralized problem, we consider the distributed trajectory design that each UAV selects its trajectory autonomously and propose the distributed route selection (DRS) scheme, which will converge to a pure strategy Nash equilibrium within a finite number of iterations.
Rethinking Convolutional Features in Correlation Filter Based Tracking
Venue: arXiv cs.CV 2019
Year: 2019

Both accuracy and efficiency are of significant importance to the task of visual object tracking. In recent years, as the surge of deep learning, Deep Convolutional NeuralNetwork (DCNN) becomes a very popular choice among the tracking community. However, due to the high computational complexity, end-to-end visual object trackers can hardly achieve an acceptable inference time and therefore can difficult to be utilized in many real-world applications. In this paper, we revisit a hierarchical deep feature-based visual tracker and found that both the performance and efficiency of the deep tracker are limited by the poor feature quality. Therefore, we propose a feature selection module to select more discriminative features for the trackers. After removing redundant features, our proposed tracker achieves significant improvements in both performance and efficiency. Finally, comparisons with state-of-the-art trackers are provided.
ICSTrace: A Malicious IP Traceback Model for Attacking Data of Industrial Control System
Venue: arXiv cs.CR 2019
Year: 2019

Considering the attacks against industrial control system are mostly organized and premeditated actions, IP traceback is significant for the security of industrial control system. Based on the infrastructure of the Internet, we have developed a novel malicious IP traceback model-ICSTrace, without deploying any new services. The model extracts the function codes and their parameters from the attack data according to the format of industrial control protocol, and employs a short sequence probability method to transform the function codes and their parameter into a vector, which characterizes the attack pattern of malicious IP addresses. Furthermore, a Partial Seeded K-Means algorithm is proposed for the pattern's clustering, which helps in tracing the attacks back to an organization. ICSTrace is evaluated basing on the attack data captured by the large-scale deployed honeypots for industrial control system, and the results demonstrate that ICSTrace is effective on malicious IP traceback in industrial control system.
Variance Reduced Local SGD with Lower Communication Complexity
Venue: arXiv cs.LG 2019
Year: 2019

To accelerate the training of machine learning models, distributed stochastic gradient descent (SGD) and its variants have been widely adopted, which apply multiple workers in parallel to speed up training. Among them, Local SGD has gained much attention due to its lower communication cost. Nevertheless, when the data distribution on workers is non-identical, Local SGD requires $O(T^{\frac{3}{4}} N^{\frac{3}{4}})$ communications to maintain its \emph{linear iteration speedup} property, where $T$ is the total number of iterations and $N$ is the number of workers. In this paper, we propose Variance Reduced Local SGD (VRL-SGD) to further reduce the communication complexity. Benefiting from eliminating the dependency on the gradient variance among workers, we theoretically prove that VRL-SGD achieves a \emph{linear iteration speedup} with a lower communication complexity $O(T^{\frac{1}{2}} N^{\frac{3}{2}})$ even if workers access non-identical datasets. We conduct experiments on three machine learning tasks, and the experimental results demonstrate that VRL-SGD performs impressively better than Local SGD when the data among workers are quite diverse.
World Programs for Model-Based Learning and Planning in Compositional State and Action Spaces
Venue: arXiv cs.LG 2019
Year: 2019

Some of the most important tasks take place in environments which lack cheap and perfect simulators, thus hampering the application of model-free reinforcement learning (RL). While model-based RL aims to learn a dynamics model, in a more general case the learner does not know a priori what the action space is. Here we propose a formalism where the learner induces a world program by learning a dynamics model and the actions in graph-based compositional environments by observing state-state transition examples. Then, the learner can perform RL with the world program as the simulator for complex planning tasks. We highlight a recent application, and propose a challenge for the community to assess world program-based planning.
Semi-Supervised Learning with Normalizing Flows
Venue: arXiv cs.LG 2019
Year: 2019

Normalizing flows transform a latent distribution through an invertible neural network for a flexible and pleasingly simple approach to generative modelling, while preserving an exact likelihood. We propose FlowGMM, an end-to-end approach to generative semi supervised learning with normalizing flows, using a latent Gaussian mixture model. FlowGMM is distinct in its simplicity, unified treatment of labelled and unlabelled data with an exact likelihood, interpretability, and broad applicability beyond image data. We show promising results on a wide range of applications, including AG-News and Yahoo Answers text data, tabular data, and semi-supervised image classification. We also show that FlowGMM can discover interpretable structure, provide real-time optimization-free feature visualizations, and specify well calibrated predictive distributions.
Online Peer-Assessment Datasets
Venue: arXiv cs.CY 2019
Year: 2019

Peer-assessment experiments were conducted among first and second year students at the University of Trento. The experiments spanned an entire semester and were conducted in five computer science courses between 2013 and 2016. Peer-assessment tasks included question and answer submission as well as answer evaluation tasks. The peer-assessment datasets are complimented by the final scores of participating students for each course. Teachers were involved in filtering out questions submitted by students on a weekly basis. Selected questions were then used in subsequent peer-assessment tasks. However, expert ratings are not included in the dataset. A major reason for this decision was that peer-assessment tasks were designed with minimal teacher supervision in mind. Arguments in favour of this approach are presented. The datasets are designed in a manner that would allow their utilization in a variety of experiments. They are reported as parsable data structures that, with intermediate processing, can be moulded into NLP or ML-ready datasets. Potential applications of interest include performance prediction and text similarity tasks.
Teaching a New Dog Old Tricks: Resurrecting Multilingual Retrieval Using Zero-shot Learning
Venue: arXiv cs.IR 2019
Year: 2019

While billions of non-English speaking users rely on search engines every day, the problem of ad-hoc information retrieval is rarely studied for non-English languages. This is primarily due to a lack of data set that are suitable to train ranking algorithms. In this paper, we tackle the lack of data by leveraging pre-trained multilingual language models to transfer a retrieval system trained on English collections to non-English queries and documents. Our model is evaluated in a zero-shot setting, meaning that we use them to predict relevance scores for query-document pairs in languages never seen during training. Our results show that the proposed approach can significantly outperform unsupervised retrieval techniques for Arabic, Chinese Mandarin, and Spanish. We also show that augmenting the English training collection with some examples from the target language can sometimes improve performance.
Intrinsic motivations and open-ended learning
Venue: arXiv cs.AI 2019
Year: 2019

There is a growing interest and literature on intrinsic motivations and open-ended learning in both cognitive robotics and machine learning on one side, and in psychology and neuroscience on the other. This paper aims to review some relevant contributions from the two literature threads and to draw links between them. To this purpose, the paper starts by defining intrinsic motivations and by presenting a computationally-driven theoretical taxonomy of their different types. Then it presents relevant contributions from the psychological and neuroscientific literature related to intrinsic motivations, interpreting them based on the grid, and elucidates the mechanisms and functions they play in animals and humans. Endowed with such concepts and their biological underpinnings, the paper next presents a selection of models from cognitive robotics and machine learning that computationally operationalise the concepts of intrinsic motivations and links them to biology concepts. The contribution finally presents some of the open challenges of the field from both the psychological/neuroscientific and computational perspectives.
From Ideation to Implications: Directions for the Internet of Things in the Home
Venue: arXiv cs.HC 2019
Year: 2019

In this paper we give a brief overview of our approaches and ongoing work for future directions of the Internet of Things (IoT) with a focus on the IoT in the home. We highlight some of our activities including tools and methods for an ideation-driven approach as well as for an implications-driven approach. We point to some findings of workshops and empirical field-studies. We show examples for new classes of idiosyncratic IoT devices, how implications emerge by (mis)using sensor data and how users interacted with IoT systems in shared spaces.
Unseen Face Presentation Attack Detection Using Class-Specific Sparse One-Class Multiple Kernel Fusion Regression
Venue: arXiv cs.CV 2019
Year: 2019

The paper addresses face presentation attack detection in the challenging conditions of an unseen attack scenario where the system is exposed to novel presentation attacks that were not present in the training step. For this purpose, a pure one-class face presentation attack detection approach based on kernel regression is developed which only utilises bona fide (genuine) samples for training. In the context of the proposed approach, a number of innovations, including multiple kernel fusion, client-specific modelling, sparse regularisation and probabilistic modelling of score distributions are introduced to improve the efficacy of the method. The results of experimental evaluations conducted on the OULU-NPU, Replay-Mobile, Replay-Attack and MSU-MFSD datasets illustrate that the proposed method compares very favourably with other methods operating in an unseen attack detection scenario while achieving very competitive performance to multi-class methods (benefiting from presentation attack data for training) despite using only bona fide samples for training.
Efficiently Realizing Interval Sequences
Venue: arXiv cs.DS 2019
Year: 2019

We consider the problem of realizable interval-sequences. An interval sequence comprises of $n$ integer intervals $[a_i,b_i]$ such that $0\leq a_i \leq b_i \leq n-1$, and is said to be graphic/realizable if there exists a graph with degree sequence, say, $D=(d_1,\ldots,d_n)$ satisfying the condition $a_i \leq d_i \leq b_i$, for each $i \in [1,n]$. There is a characterisation (also implying an $O(n)$ verifying algorithm) known for realizability of interval-sequences, which is a generalization of the Erdos-Gallai characterisation for graphic sequences. However, given any realizable interval-sequence, there is no known algorithm for computing a corresponding graphic certificate in $o(n^2)$ time. In this paper, we provide an $O(n \log n)$ time algorithm for computing a graphic sequence for any realizable interval sequence. In addition, when the interval sequence is non-realizable, we show how to find a graphic sequence having minimum deviation with respect to the given interval sequence, in the same time. Finally, we consider variants of the problem such as computing the most regular graphic sequence, and computing a minimum extension of a length $p$ non-graphic sequence to a graphic one.